WNF03000 Basics of Machine Learning

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School Graduate School of Humanities and Social Sciences (Master's Course) Division of Educational Sciences Education Data Science Program

Lecture Code WNF03000 Subject Classification Specialized Education

Subject Name 機械学習の基礎

Subject Name
（Katakana）

Subject Name in
English Basics of Machine Learning

Instructor TANAKA HIDEYUKI,HARADA YUSUKE

Instructor
(Katakana) タナカ　ヒデユキ,ハラダ　ユウスケ

Campus Higashi-Hiroshima Semester/Term 1st-Year, First Semester, 2Term

Days, Periods, and Classrooms (2T) Weds5-8：EDU K115

Lesson Style Seminar Lesson Style
(More Details) Face-to-face, Online (on-demand)

Credits 2.0 Class Hours/Week 4 Language of Instruction J : Japanese

Course Level 5 : Graduate Basic

Course Area（Area） 24 : Social Sciences

Course Area（Discipline） 07 : Education

Eligible Students Education Data Science Program

Keywords Machine Learning, Data science, AI, Social Sciences, Education

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline In this course, you will learn the fundamentals of programming and machine learning, which are essential for digital transformation (DX) in education, as well as the application of data science and AI

Class Schedule Lesson 1: Guidance. Setting up the execution environment. Basics of Python (1).
Lesson 2: Basics of Python (2): Writing and using conditional statements, writing loops and typical patterns.
Lesson 3: Basics of Python (3): Major collection types such as lists, tuples, and dictionaries (dict). Iteration and list comprehensions.
Lesson 4: Basics of Python (4): Function definitions, scope, and related mechanisms. Basic usage of modules and packages.
Lesson 5: Basics of NumPy (1): Overview and features. Basic operations with arrays.
Lesson 6: Basics of NumPy (2): Operations between arrays, broadcasting, and using NumPy's universal (mathematical) functions and statistical functions.
Lesson 7: Data manipulation with pandas (1): Concepts of data structures such as Series and DataFrame.
Lesson 8: Data manipulation with pandas (2): Practical data cleaning and processing techniques using pandas. Handling missing values, duplicate data, etc.
Lesson 9: Data manipulation with pandas (3): Using pandas for visualization and library integration. Overview of advanced features like time series data and multi-indexing.
Lesson 10: Visualization with Matplotlib (1): Basics of data visualization and using Matplotlib. Creating line charts, scatter plots, bar charts, etc.
Lesson 11: Visualization with Matplotlib (2): Advanced visualization techniques with Matplotlib. Displaying multiple subplots, heatmaps, and representing multidimensional data.
Lesson 12: Basics of text data analysis (1): Basic concepts for handling text data (natural language).
Lesson 13: Basics of text data analysis (2): Understanding the process of converting text into vectors for use in machine learning and statistical methods.
Lesson 14: Machine learning with scikit-learn (1): Overview of machine learning and key techniques (supervised learning, unsupervised learning). Implementing basic regression and classification tasks with scikit-learn.
Lesson 15: Machine learning with scikit-learn (2): A wide range of machine learning methods, including classification problems and clustering.

Students are required to submit reports.

Text/Reference
Books,etc. The textbooks will be assigned in the class.

PC or AV used in
Class,etc. Visual Materials, Microsoft Teams, moodle

(More Details)

Learning techniques to be incorporated Discussions, Post-class Report

Suggestions on
Preparation and
Review Lesson 1: Understand the flow of the course. Ensure that the environment is set up properly.
Lessons 1-4: Deepen your understanding of Python.
Lessons 5-6: Understand how to use NumPy.
Lessons 7-9: Become proficient in using pandas.
Lessons 10-11: Deepen your understanding of data visualization.
Lessons 12-13: Deepen your understanding of text data analysis.
Lessons 14-15: Learn how to use machine learning with scikit-learn.

Requirements

Grading Method Comprehensive evaluation of attitude to class and report

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2025Year	School/Graduate School	Graduate School of Humanities and Social Sciences (Master's Course) Division of Educational Sciences Education Data Science Program
Lecture Code	WNF03000	Subject Classification	Specialized Education
Subject Name	機械学習の基礎
Subject Name （Katakana）
Subject Name in English	Basics of Machine Learning
Instructor	TANAKA HIDEYUKI,HARADA YUSUKE
Instructor (Katakana)	タナカ　ヒデユキ,ハラダ　ユウスケ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, First Semester, 2Term
Days, Periods, and Classrooms	(2T) Weds5-8：EDU K115
Lesson Style	Seminar	Lesson Style (More Details)	Face-to-face, Online (on-demand)

Credits	2.0	Class Hours/Week	4	Language of Instruction	J : Japanese
Course Level	5 : Graduate Basic
Course Area（Area）	24 : Social Sciences
Course Area（Discipline）	07 : Education
Eligible Students	Education Data Science Program
Keywords	Machine Learning, Data science, AI, Social Sciences, Education
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	In this course, you will learn the fundamentals of programming and machine learning, which are essential for digital transformation (DX) in education, as well as the application of data science and AI
Class Schedule	Lesson 1: Guidance. Setting up the execution environment. Basics of Python (1). Lesson 2: Basics of Python (2): Writing and using conditional statements, writing loops and typical patterns. Lesson 3: Basics of Python (3): Major collection types such as lists, tuples, and dictionaries (dict). Iteration and list comprehensions. Lesson 4: Basics of Python (4): Function definitions, scope, and related mechanisms. Basic usage of modules and packages. Lesson 5: Basics of NumPy (1): Overview and features. Basic operations with arrays. Lesson 6: Basics of NumPy (2): Operations between arrays, broadcasting, and using NumPy's universal (mathematical) functions and statistical functions. Lesson 7: Data manipulation with pandas (1): Concepts of data structures such as Series and DataFrame. Lesson 8: Data manipulation with pandas (2): Practical data cleaning and processing techniques using pandas. Handling missing values, duplicate data, etc. Lesson 9: Data manipulation with pandas (3): Using pandas for visualization and library integration. Overview of advanced features like time series data and multi-indexing. Lesson 10: Visualization with Matplotlib (1): Basics of data visualization and using Matplotlib. Creating line charts, scatter plots, bar charts, etc. Lesson 11: Visualization with Matplotlib (2): Advanced visualization techniques with Matplotlib. Displaying multiple subplots, heatmaps, and representing multidimensional data. Lesson 12: Basics of text data analysis (1): Basic concepts for handling text data (natural language). Lesson 13: Basics of text data analysis (2): Understanding the process of converting text into vectors for use in machine learning and statistical methods. Lesson 14: Machine learning with scikit-learn (1): Overview of machine learning and key techniques (supervised learning, unsupervised learning). Implementing basic regression and classification tasks with scikit-learn. Lesson 15: Machine learning with scikit-learn (2): A wide range of machine learning methods, including classification problems and clustering. Students are required to submit reports.
Text/Reference Books,etc.	The textbooks will be assigned in the class.
PC or AV used in Class,etc.	Visual Materials, Microsoft Teams, moodle
(More Details)
Learning techniques to be incorporated	Discussions, Post-class Report
Suggestions on Preparation and Review	Lesson 1: Understand the flow of the course. Ensure that the environment is set up properly. Lessons 1-4: Deepen your understanding of Python. Lessons 5-6: Understand how to use NumPy. Lessons 7-9: Become proficient in using pandas. Lessons 10-11: Deepen your understanding of data visualization. Lessons 12-13: Deepen your understanding of text data analysis. Lessons 14-15: Learn how to use machine learning with scikit-learn.
Requirements
Grading Method	Comprehensive evaluation of attitude to class and report
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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